Junior Data Scientist
Securitas Group
Securitas is a world-leading safety and security solutions partner that helps make your world a safer place. By leveraging technology in partnership with our clients, we offer a broad portfolio of value-enhancing services and solutions integrated across the security value chain – from on-site services to advanced monitoring, comprehensive risk prediction and advisory services.
With around 322 000 employees in 44 markets, our innovative, holistic approach with local and global expertise makes us a trusted business partner to many of the world’s best-known companies. Benefitting from almost nine decades of deep experience and guided by our values of integrity, vigilance, and helpfulness, we create sustainable value by helping our clients optimize their operations and protect what matters most - their people and assets.
AI Team at Securitas
At Securitas, our colleagues show up every day to help keep communities and organizations safe. Our job in the AI team is to make sure they're equipped with the best tools and intelligence possible.
We are Securitas' Specialized AI Team - the internal center of excellence for advanced, custom AI. We don't work on generic tools or off-the-shelf solutions. We build the AI capabilities that require deep technical and domain expertise, and that directly move the needle on how Securitas operates at scale.
About the role
You will be joining a small, focused team and growing into a strong technical contributor. You'll work across the full ML lifecycle - from messy data and ambiguous problems to models that run in production and create real operational impact. You'll contribute to meaningful projects from day one, with guidance and mentorship from senior team members. Over time, you'll take on increasing ownership as you build your skills and confidence.
The stack we work with
Python · PyTorch · LLMs (OpenAI, Gemini, Claude, open-source) · RAG pipelines, Hugging Face · Python analytical tools (DuckDB, polars, Pandas, and more) · Streamlit & Dash · Claude Code · GitHub Copilot · React · Databricks · Docker · SQL/NoSQL · Azure/GCP
Responsibilities
Machine Learning & GenAI
Contribute to the development of LLM-powered pipelines that extract structure and insight from unstructured text - incident reports, operational logs, client data.
Support the design and testing of GenAI architectures (e.g. RAG), prompt strategies, and evaluation frameworks, learning what "good" looks like in production.
Training and evaluation och machine learning models - classification, regression, and forecasting.
Assist in building and iterating on workforce management models - demand forecasting, shift scheduling optimization, and attrition modeling.
Help develop client churn models that surface early, actionable retention signals for the business.
Engineering & Delivery
Work across the end-to-end ML lifecycle: data exploration, feature engineering, modelling, evaluation, and deployment support.
Write clean, readable, and testable Python code - and keep improving your software engineering habits with support from the team.
Learn and apply MLOps fundamentals: model serving, monitoring, CI/CD, Docker, and reproducible pipelines.
Use modern AI coding tools (GitHub Copilot, Claude Code) to work more productively and iterate faster.
Collaboration & Communication
Translate business questions into data problems, with guidance from senior data scientists.
Present findings and model results to internal stakeholders in a clear, honest, and accessible way.
Work collaboratively across data, engineering, and business teams.
What you'll bring
Must-haves
A Degree in a quantitative field (Data Science, Computer Science, Statistics, Engineering, or related) or equivalent practical experience.
Solid Python skills and a genuine interest in writing good, maintainable code.
Hands-on experience with core ML concepts and NLP - whether from coursework, personal projects, internships, or early-career work.
Familiarity with LLMs: you've worked with them beyond basic prompting and are curious about how they fail and how to evaluate them.
A scientific, evidence-based mindset - you ask "why?" when results look surprising, and you check your own assumptions.
Clear, honest communication: you can explain a method, a result, or a limitation in plain language.
Nice-to-haves
Exposure to MLOps concepts: deployment, monitoring, Docker, or CI/CD pipelines.
Experience with Databricks or similar large-scale data platforms.
Familiarity with LLM evaluation frameworks (Ragas, LangSmith, or similar).
Interest or experience in forecasting, scheduling, or operational optimisation problems.
Comfort building simple interactive data tools (Streamlit, Dash, or similar).
Background in a domain where decisions have real operational consequences — logistics, healthcare, security, or similar.
Working conditions
The role is open for candidates based in Malmö or Stockholm (with preference for applicants in Malmö). It's a hybrid working model.
What we offer
At Securitas we believe in doing the right thing and doing it well. For our customers and our employees. Our employees come from all walks of life and bring with them many talents and perspectives. We aim for diverse representation throughout the company, and we are committed to equal pay, safe working conditions, gender balance and an inclusive work environment with a wide range of skills and development opportunities.
If this sounds like the right next step in your professional career, don't hesitate and apply!
- Department
- Securitas IT
- Locations
- Sweden, Malmö, Stockholm
- Remote status
- Hybrid
Open positions at Securitas IT & Securitas Risk Intelligence
We Make A Difference Every Day!
We work in an international, explorative, hands-on and dynamic business environment with customers and users in focus.
Our core values: Integrity, Vigilance and Helpfulness - are foundations that enable us to build trust with customers, colleagues, partners and our community.
We love going to work and doing what we do.
About Securitas
As a world-leading safety and security solutions partner, Securitas sees what others miss. Since 1934, Securitas has defined – and redefined – the industry, evolving to meet and anticipate organizations' needs. Today, we’re leading the industry’s digital transformation, and we remain committed to upholding our legacy of innovation and progress.